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Network Averaging: a technique for determining a proxy for the dynamics of networks

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  • Bell, William Paul

Abstract

The main aim of this paper is to introduce the network averaging technique. This technique is introduced because accurately determining the structure of real networks can be difficult and the network averaging technique provides a proxy for real networks. A second aim is to introduce the adaptive interactive expectations (AIE) model, which uses a ‘pressure to change profit expectations index’ to replace the utility curve maximising agent concept. The AIE model has an interactive expectations network, which is difficult to determine, so suitable to illustrate network averaging. The AIE model is tested against the Dun and Bradstreet Profit Expectations Survey. The paper finds network averaging improves the predictive performance of AIE over its benchmarks: the rational expectations hypothesis and the adaptive expectations model. The network averaging technique could be adapted to other situations where there are endogenous effects acting through difficult to measure networks. The AIE model could be readily applied to other forms of expectations and as a replacement for the utility curve maximising agent. Finally, in this paper AIE models profit expectations, which are an important issue in their own right because they affect investment decisions and whether one business will extend credit to another business.

Suggested Citation

  • Bell, William Paul, 2009. "Network Averaging: a technique for determining a proxy for the dynamics of networks," MPRA Paper 38026, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:38026
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    File URL: https://mpra.ub.uni-muenchen.de/38026/1/MPRA_paper_38026.pdf
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    References listed on IDEAS

    as
    1. J. M. Keynes, 1937. "The General Theory of Employment," The Quarterly Journal of Economics, Oxford University Press, vol. 51(2), pages 209-223.
    2. Jürgen Eichberger & David Kelsey & Burkhard C. Schipper, 2009. "Ambiguity and social interaction," Oxford Economic Papers, Oxford University Press, vol. 61(2), pages 355-379, April.
    3. Mark Bowden & Stuart McDonald, 2006. "Social interaction, herd behaviour and the formation of agent expectations," Computing in Economics and Finance 2006 178, Society for Computational Economics.
    4. John Foster & Burkhard Flieth, 2002. "Interactive expectations," Journal of Evolutionary Economics, Springer, vol. 12(4), pages 375-395.
    5. Herbert Dawid & Michael Neugart, 2011. "Agent-based Models for Economic Policy Design," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 37(1), pages 44-50.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Networks; interactive; adaptive; model averaging; profit;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • B52 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Historical; Institutional; Evolutionary
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • B53 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Austrian

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